Construction of wavelet dictionaries for ECG modeling

Černá, Dana; Rebollo-Neira, Laura

doi:10.1016/j.mex.2021.101314

Cited by 4 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For screening of the training set we have used the 9/7 Cohen-Daubechies-Feauveau (cbf97) wavelet family with b = 0.25, j 0 = 2 and j = 3, 4, 5, 6, which introduces a redundancy factor of 2.67. This dictionary was generated with the software described in [26] available on [27]. The scaling and wavelet prototypes for the cbf97 wavelet family are shown in Fig.…”

Section: Screening the Data Training Setsmentioning

confidence: 99%

Sparsity based morphological identification of heartbeats

Rebollo-Neira¹,

Battikh²,

Watt³

2023

Preprint

View full text Add to dashboard Cite

Background: The electrocardiogram (ECG) is one of the most common primary tests to evaluate the health of the heart. Reliable automatic interpretation of ECG records is crucial to the goal of improving public health. It can enable a safe inexpensive monitoring. This work presents a new methodology for morphological identification of heartbeats, which is placed outside the usual machine learning framework.Method: The proposal considers the sparsity of the representation of a heartbeat as a parameter for morphological identification. The approach involves greedy algorithms for selecting elements from redundant dictionaries, which should be previously learnt from examples of the classes to be identified. Using different metrics of sparsity, the dictionary rendering the smallest sparsity value, for the equivalent approximation quality of a new heartbeat, classifies the morphology of that beat. This study focuses on a procedure of learning the dictionaries for representing heartbeats and compares several metrics of sparsity for morphological identification on the basis of those metrics.Results: The suitability of the method is illustrated by binary differentiation of Normal and Ventricular heartbeats in the MIT-BIH Arrhythmia data set. In general classification 99.7% of the Normal beats and 97.6% of the Ventricular beats in the testing sets are correctly identified. In interpatient assessment 91.8% of the Normal beats and 91.0% of Ventricular beats are correctly identified. Even more important than these scores is the fact that they are produced on the bases of a single parameter.Conclusions: The numerical tests, designed to emphasise the interpretability and reliability of the approach, demonstrate the potential of the method to contribute towards the development of a well grounded expert system for classification of heartbeats in ECG records.

show abstract

Section: Screening the Data Training Setsmentioning

confidence: 99%

Sparsity based morphological identification of heartbeats

Rebollo-Neira¹,

Battikh²,

Watt³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The authors Dana Černá et al [205] provided the complete description to build wavelet dictionaries with reduced dimensionality for modelling the ECG signals, these dictionaries are created from known wavelet families. Each dictionary is created by taking the models from a wavelet basis and translating them in a smaller step than the wavelet basis itself.…”

Section: Cs Implementations-sensing and Sparsifying Matricesmentioning

confidence: 99%

Review on Compressive Sensing Algorithms for ECG Signal for IoT Based Deep Learning Framework

Kumar

Ramachandran

2022

Applied Sciences

View full text Add to dashboard Cite

Nowadays, healthcare is becoming very modern, and the support of Internet of Things (IoT) is inevitable in a personal healthcare system. A typical personal healthcare system acquires vital parameters from human users and stores them in a cloud platform for further analysis. Acquiring fundamental biomedical signal, such as with the Electrocardiograph (ECG), is also considered for specific disease analysis in personal healthcare systems. When such systems are scaled up, there is a heavy demand for internet channel capacity to accommodate real time seamless flow of discrete samples of biomedical signals. So, there is a keen need for real time data compression of biomedical signals. Compressive Sensing (CS) has recently attracted more interest due to its compactness and its feature of the faithful reconstruction of signals from fewer linear measurements, which facilitates less than Shannon’s sampling rate by exploiting the signal sparsity. The most common biomedical signal that is to be analyzed is the ECG signal, as the prediction of heart failure at an early stage can save a human life. This review is for a vast use-case of IoT framework in which CS measurements of ECG are acquired, communicated through Internet to a server, and the arrhythmia are analyzed using Machine learning (ML). Assuming this use-case specific for ECG, in this review many technical aspects are considered regarding various research components. The key aspect is on the investigation of the best sensing method, and to address this, various sensing matrices are reviewed, analyzed and recommended. The next aspect is the selection of the optimal sparsifying method, and the review recommends unexplored ECG compression algorithms as sparsifying methods. The other aspects are optimum reconstruction algorithms, best hardware implementations, suitable ML methods and effective modality of IoT. In this review all these components are considered, and a detailed review is presented which enables us to orchestrate the use-case specified above. This review focuses on the current trends in CS algorithms for ECG signal compression and its hardware implementation. The key to successful reconstruction of the CS method is the right selection of sensing and sparsifying matrix, and there are many unexplored sparsifying methods for the ECG signal. In this review, we shed some light on new possible sparsifying techniques. A detailed comparison table of various CS algorithms, sensing matrix, sparsifying techniques with different ECG dataset is tabulated to quantify the capability of CS in terms of appropriate performance metrics. As per the use-case specified above, the CS reconstructed ECG signals are to be subjected to ML analysis, and in this review the compressive domain inference approach is discussed. The various datasets, methodologies and ML models for ECG applications are studied and their model accuracies are tabulated. Mostly, the previous research on CS had studied the performance of CS using numerical simulation, whereas there are some good attempts for hardware implementations for ECG applications, and we studied the uniqueness of each method and supported the study with a comparison table. As a consolidation, we recommend new possibilities of the research components in terms of new transforms, new sparsifying methods, suggestions for ML approaches and hardware implementation.

show abstract